A Fessl, S Feyertag, V Pammer – In Proceeding of 15th International Conference on Knowledge Technologies and Data-driven…, 2015

This paper presents a case study on co-designing digital technologies for knowledge management and data-driven business for an SME. The goal of the case study was to analyse the status quo of technology usage and to develop design suggestions in form of mock-ups tailored to the company's needs. We used…

The Social Semantic Server: A Flexible Framework to Support Informal Learning at the Workplace

Informal learning at the workplace includes a multitude of processes. Respective activities can be categorized into multiple perspectives on informal learning, such as reflection, sensemaking, help seeking and maturing of collective knowledge. Each perspective raises requirements with respect to the technical support, this is why an integrated solution relying on…

In this paper, we present a scalable hotel recommender system for TripRebel, a new online booking portal. On the basis of the opensource enterprise search platform Apache Solr, we developed a system architecture with Web-based services to interact with indexed data at large scale as well as to provide hotel…

To date, the evaluation of tag recommender algorithms has mostly been conducted in limited ways, including p-core pruned datasets, a small set of compared algorithms and solely based on recommender accuracy. In this study, we use an open-source evaluation framework to compare a rich set of state-of-the-art algorithms in six…

In this paper, we present work-in-progress on a recommender system based on Collaborative Filtering that exploits location information gathered by indoor positioning systems. This approach allows us to provide recommendations for "extreme" cold-start users with absolutely no item interaction data available, where methods based on Matrix Factorization would not work.…

ScaR: Towards a Real-Time Recommender Framework Following the Microservices Architecture

Various recommender frameworks have been proposed, but still there is a lack of work that addresses important aspects like: immediately considering streaming data within the recommendation process; scalability of the recommender system; real-time recommendation based on different context dependent data. To bridge these gaps, we contribute with a novel recommender…

In-App Reflection Guidance for Workplace Learning

In-app reflection guidance for workplace learning means motivating and guiding users to reflect on their working and learning, based on users' activities captured by the app. In this paper, we present a generic concept for such in-app reflection guidance for workplace learning, its implementation in three dierent applications, and its…

The Value of Self-Tracking and the Added Value of Coaching in the Case of Improving Time Management

We report two 6-week studies, each with 10 participants, on improving time management. In each study a different interventions was administered, in parallel to otherwise regular work: In the self-tracking setting, participants used only an activity logging tool to track their time use and a reflective practice, namely daily review…

Cross vertical aggregated search is a special form of meta search, were multiple search engines from different domains and varying behaviour are combined to produce a single search result for each query. Such a setting poses a number of challenges, among them the question of how to best evaluate the…

The objective of the EEXCESS (Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources) project is to develop a system that can automatically recommend helpful and novel content to knowledge workers. The EEXCESS system can be integrated into existing software user interfaces as plugins which will extract topics and suggest…

Underspecified search queries can be performed via result list diversification approaches, which are often computationally complex and require longer response times. In this paper, we explore an alternative, and more efficient way to diversify the result list based on query expansion. To that end, we used a knowledge base pseudo-relevance…

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